
> # This function rounds number to 3 digits and converts to a string
> Trim <- function( x, digits=3 ) {
+   outVal <- format( round(x,digits=digits)  .... [TRUNCATED] 

> # This function converts p-values into stars for use in output table
> Stars <- function( pValue ) {
+   outval <- rep( "   ", times=length(pValue)  .... [TRUNCATED] 

> # This function will be used to assemble regression output into output tables.
> # j is the output of an OLS regression
> # numPlaces provides the n .... [TRUNCATED] 

> # Read in the tab-delimited data file
> load("REStatSubmission_FRB.RData")

> #######################################################################################
> ### Table 1 - Summary Statistics 
> ###################### .... [TRUNCATED] 

> table1.output <- rbind( table1.output, c("Center City", Trim(weighted.mean(myData$CENTER,w=myData$COUNT), digits=2) ) )

> table1.output <- rbind( table1.output, c("Median House Value", Trim(weighted.mean(myData$CUML,w=myData$COUNT), digits=2) ) )

> table1.output <- rbind( table1.output, c("Median Age", Trim(weighted.mean(myData$MEDAGE,w=myData$COUNT), digits=2) ) )

> table1.output <- rbind( table1.output, c("% 65 or Older", Trim(weighted.mean(myData$OVER65,w=myData$COUNT), digits=2) ) )

> table1.output <- rbind( table1.output, c("Unemployment Rate", Trim(weighted.mean(myData$UNEMRT,w=myData$COUNT), digits=2) ) )

> table1.output <- rbind( table1.output, c("% Minority", Trim(weighted.mean(myData$minpct,w=myData$COUNT), digits=2) ) )

> table1.output <- rbind( table1.output, c("Owner Occupancy Rate", Trim(weighted.mean(myData$ownpct,w=myData$COUNT), digits=2) ) )

> table1.output <- rbind( table1.output, c("Vacancy Rate", Trim(weighted.mean(myData$vacpct,w=myData$COUNT), digits=2) ) )

> table1.output <- rbind( table1.output, c("2000 Relative Income", Trim(weighted.mean(myData$relinc,w=myData$COUNT), digits=2) ) )

> table1.output <- rbind( table1.output, c("1990 Relative Income", Trim(weighted.mean(myData$relinc90,w=myData$COUNT), digits=2) ) )

> table1.output <- rbind( table1.output, c("2001 Credit Score", Trim(weighted.mean(myData$SCORE2000,w=myData$COUNT), digits=2) ) )

> table1.output <- rbind( table1.output, c("2004 Credit Score", Trim(weighted.mean(myData$SCORE2004,w=myData$COUNT), digits=2) ) )

> table1.output <- rbind( table1.output, c("% High PTI", Trim(weighted.mean(myData$hidti_20046,w=myData$COUNT), digits=2) ) )

> table1.output <- rbind( table1.output, c("% High Rate", Trim(weighted.mean(myData$hirate_20046,w=myData$COUNT), digits=2) ) )

> table1.output <- rbind( table1.output, c("% Piggyback", Trim(weighted.mean(myData$pig_20046,w=myData$COUNT), digits=2) ) )

> table1.output <- rbind( table1.output, c("% No Income", Trim(weighted.mean(myData$zerinc_20046,w=myData$COUNT), digits=2) ) )

> table1.output <- rbind( table1.output, c("% LMI", Trim(weighted.mean(myData$lowinc_20046,w=myData$COUNT), digits=2) ) )

> table1.output <- rbind( table1.output, c("% Below Median Income", Trim(weighted.mean(myData$midinc_20046,w=myData$COUNT), digits=2) ) )

> table1.output <- rbind( table1.output, c("2001 Dep Out Share", Trim(weighted.mean(myData$shr.dep.out_2001,w=myData$COUNT), digits=2) ) )

> table1.output <- rbind( table1.output, c("2001 Dep In Share", Trim(weighted.mean(myData$shr.dep.in_2001,w=myData$COUNT), digits=2) ) )

> table1.output <- rbind( table1.output, c("2001 Aff Out Share", Trim(weighted.mean(myData$shr.aff.out_2001,w=myData$COUNT), digits=2) ) )

> table1.output <- rbind( table1.output, c("2001 Aff In Share", Trim(weighted.mean(myData$shr.aff.in_2001,w=myData$COUNT), digits=2) ) )

> table1.output <- rbind( table1.output, c("2001 Credit Union Share", Trim(weighted.mean(myData$shr.cu_2001,w=myData$COUNT), digits=2) ) )

> table1.output <- rbind( table1.output, c("2001 Independent Share", Trim(weighted.mean(myData$shr.ind_2001,w=myData$COUNT), digits=2) ) )

> table1.output <- rbind( table1.output, c("2004-6 Dep Out Share", Trim(weighted.mean(myData$shr.dep.out_20046,w=myData$COUNT), digits=2) ) )

> table1.output <- rbind( table1.output, c("2004-6 Dep In Share", Trim(weighted.mean(myData$shr.dep.in_20046,w=myData$COUNT), digits=2) ) )

> table1.output <- rbind( table1.output, c("2004-6 Aff Out Share", Trim(weighted.mean(myData$shr.aff.out_20046,w=myData$COUNT), digits=2) ) )

> table1.output <- rbind( table1.output, c("2004-6 Aff In Share", Trim(weighted.mean(myData$shr.aff.in_20046,w=myData$COUNT), digits=2) ) )

> table1.output <- rbind( table1.output, c("2004-6 Credit Union Share", Trim(weighted.mean(myData$shr.cu_20046,w=myData$COUNT), digits=2) ) )

> table1.output <- rbind( table1.output, c("2004-6 Independent Share", Trim(weighted.mean(myData$shr.ind_20046,w=myData$COUNT), digits=2) ) )

> table1.output <- rbind( table1.output, c("2001 Dep Out Purchases", Trim(weighted.mean(myData$pur.dep.out_2001,w=myData$COUNT), digits=2) ) )

> table1.output <- rbind( table1.output, c("2001 Dep In Purchases", Trim(weighted.mean(myData$pur.dep.in_2001,w=myData$COUNT), digits=2) ) )

> table1.output <- rbind( table1.output, c("2001 Aff Out Purchases", Trim(weighted.mean(myData$pur.aff.out_2001,w=myData$COUNT), digits=2) ) )

> table1.output <- rbind( table1.output, c("2001 Aff In Purchases", Trim(weighted.mean(myData$pur.aff.in_2001,w=myData$COUNT), digits=2) ) )

> table1.output <- rbind( table1.output, c("2001 Credit Union Purchases", Trim(weighted.mean(myData$pur.cu_2001,w=myData$COUNT), digits=2) ) )

> table1.output <- rbind( table1.output, c("2001 Independent Purchases", Trim(weighted.mean(myData$pur.ind_2001,w=myData$COUNT), digits=2) ) )

> table1.output <- rbind( table1.output, c("2001 GSE Sales", Trim(weighted.mean(myData$gse_2001,w=myData$COUNT),digits=2) ) )

> table1.output <- rbind( table1.output, c("2004-6 Dep Out Purchases", Trim(weighted.mean(myData$pur.dep.out_20046,w=myData$COUNT), digits=2) ) )

> table1.output <- rbind( table1.output, c("2004-6 Dep In Purchases", Trim(weighted.mean(myData$pur.dep.in_20046,w=myData$COUNT), digits=2) ) )

> table1.output <- rbind( table1.output, c("2004-6 Aff Out Purchases", Trim(weighted.mean(myData$pur.aff.out_20046,w=myData$COUNT), digits=2) ) )

> table1.output <- rbind( table1.output, c("2004-6 Aff In Purchases", Trim(weighted.mean(myData$pur.aff.in_20046,w=myData$COUNT), digits=2) ) )

> table1.output <- rbind( table1.output, c("2004-6 Credit Union Purchases", Trim(weighted.mean(myData$pur.cu_20046,w=myData$COUNT), digits=2) ) )

> table1.output <- rbind( table1.output, c("2004-6 Independent Purchases", Trim(weighted.mean(myData$pur.ind_20046,w=myData$COUNT), digits=2) ) )

> table1.output <- rbind( table1.output, c("2004-6 GSE Sales", Trim(weighted.mean(myData$gse_20046,w=myData$COUNT),digits=2) ) )

> #######################################################################################
> ### Table 2 
> ########################################### .... [TRUNCATED] 

> table2.b <- lm(formula=hidti_20046 ~ as.factor(MSA),
+                data=myData,
+                weights=loans_20046,
+                subset=( M .... [TRUNCATED] 

> table2.c <- lm(formula=hirate_20046 ~ as.factor(MSA),
+                data=myData,
+                weights=loans_20046,
+                subset=(  .... [TRUNCATED] 

> # table2.1 to table2.5 estimate the models presented in Table 2
> table2.1 <- lm(formula=delrate ~ shr.dep.out_2001 + shr.dep.in_2001 + shr.aff.out_ .... [TRUNCATED] 

> table2.2 <- lm(formula=delrate ~ shr.dep.out_20046 + shr.dep.in_20046 + shr.aff.out_20046 + shr.aff.in_20046 + shr.cu_20046 +
+                      .... [TRUNCATED] 

> table2.3 <- lm(formula=delrate ~ shr.dep.out_20046 + shr.dep.in_20046 + shr.aff.out_20046 + shr.aff.in_20046 + shr.cu_20046 +
+                      .... [TRUNCATED] 

> table2.4 <- lm(formula=hidti_20046 ~ shr.dep.out_2001 + shr.dep.in_2001 + shr.aff.out_2001 + shr.aff.in_2001 + shr.cu_2001 +
+                       .... [TRUNCATED] 

> table2.5 <- lm(formula=hirate_20046 ~ shr.dep.out_2001 + shr.dep.in_2001 + shr.aff.out_2001 + shr.aff.in_2001 + shr.cu_2001 +
+                      .... [TRUNCATED] 

> # Assemble the coefficients into an output table
> table2.1.coefs <- makeTable( table2.1, numPlaces=13 )

> table2.2.coefs <- makeTable( table2.2, numPlaces=13 )

> table2.3.coefs <- makeTable( table2.3, numPlaces=13 )

> table2.4.coefs <- makeTable( table2.4, numPlaces=13 )

> table2.5.coefs <- makeTable( table2.5, numPlaces=13 )

> table2.names <- dimnames( summary(table2.1)$coefficients )[[1]][2:13]

> table2.coefs <- cbind( table2.names, table2.1.coefs, table2.2.coefs, table2.3.coefs, table2.4.coefs, table2.5.coefs )

> # This next block of code calculates the within-MSA R-squared for each estimation, based
> # on the SSEs from each estimation and from the models es .... [TRUNCATED] 

> table2.b.sse <- weighted.mean(x=table2.b$residuals^2, w=table2.b$weights)*length(table2.b$weights)

> table2.c.sse <- weighted.mean(x=table2.c$residuals^2, w=table2.c$weights)*length(table2.c$weights)

> table2.1.sse <- weighted.mean(x=table2.1$residuals^2, w=table2.1$weights)*length(table2.1$residuals)

> table2.2.sse <- weighted.mean(x=table2.2$residuals^2, w=table2.2$weights)*length(table2.2$residuals)

> table2.3.sse <- weighted.mean(x=table2.3$residuals^2, w=table2.3$weights)*length(table2.3$residuals)

> table2.4.sse <- weighted.mean(x=table2.4$residuals^2, w=table2.4$weights)*length(table2.4$residuals)

> table2.5.sse <- weighted.mean(x=table2.5$residuals^2, w=table2.5$weights)*length(table2.5$residuals)

> table2.1.wR2 <- Trim( 1 - table2.1.sse/table2.a.sse, digits=3 )

> table2.2.wR2 <- Trim( 1 - table2.2.sse/table2.a.sse, digits=3 )

> table2.3.wR2 <- Trim( 1 - table2.3.sse/table2.a.sse, digits=3 )

> table2.4.wR2 <- Trim( 1 - table2.4.sse/table2.b.sse, digits=3 )

> table2.5.wR2 <- Trim( 1 - table2.5.sse/table2.c.sse, digits=3 )

> table2.wR2 <- cbind( "R-squared", table2.1.wR2, table2.2.wR2, table2.3.wR2, table2.4.wR2, table2.5.wR2 )

> # Report the number of observations in each estimation
> table2.1.obs <- length(table2.1$model[,1])

> table2.2.obs <- length(table2.2$model[,2])

> table2.3.obs <- length(table2.3$model[,2])

> table2.4.obs <- length(table2.4$model[,2])

> table2.5.obs <- length(table2.5$model[,2])

> table2.obs <- cbind( "Observations", table2.1.obs, table2.2.obs, table2.3.obs, table2.4.obs, table2.5.obs)

> # Report the mean of the dependent variable in each estimation
> table2.1.ybar <- Trim( weighted.mean(x=table2.1$model[,1], w=table2.1$weights), dig .... [TRUNCATED] 

> table2.2.ybar <- Trim( weighted.mean(x=table2.2$model[,1], w=table2.2$weights), digits=3 )

> table2.3.ybar <- Trim( weighted.mean(x=table2.3$model[,1], w=table2.3$weights), digits=3 )

> table2.4.ybar <- Trim( weighted.mean(x=table2.4$model[,1], w=table2.4$weights), digits=3 )

> table2.5.ybar <- Trim( weighted.mean(x=table2.5$model[,1], w=table2.5$weights), digits=3 )

> table2.ybar <- cbind( "Mean y", table2.1.ybar, table2.2.ybar, table2.3.ybar, table2.4.ybar, table2.5.ybar)

> # Conduct the three statistical tests (t1, t2, and t3) for each specification
> table2.1.t1 <- linearHypothesis(table2.1,"shr.dep.out_2001 = shr.dep ..." ... [TRUNCATED] 

> table2.1.t2 <- linearHypothesis(table2.1,"shr.aff.out_2001 = shr.aff.in_2001",test="F")

> table2.1.t3 <- linearHypothesis(table2.1,c("shr.dep.out_2001 = shr.dep.in_2001","shr.aff.out_2001 = shr.aff.in_2001"),test="F")

> table2.2.t1 <- linearHypothesis(table2.2,"shr.dep.out_20046 = shr.dep.in_20046",test="F")

> table2.2.t2 <- linearHypothesis(table2.2,"shr.aff.out_20046 = shr.aff.in_20046",test="F")

> table2.2.t3 <- linearHypothesis(table2.2,c("shr.dep.out_20046 = shr.dep.in_20046","shr.aff.out_20046 = shr.aff.in_20046"),test="F")

> table2.3.t1 <- linearHypothesis(table2.3,"shr.dep.out_20046 = shr.dep.in_20046",test="F")

> table2.3.t2 <- linearHypothesis(table2.3,"shr.aff.out_20046 = shr.aff.in_20046",test="F")

> table2.3.t3 <- linearHypothesis(table2.3,c("shr.dep.out_20046 = shr.dep.in_20046","shr.aff.out_20046 = shr.aff.in_20046"),test="F")

> table2.4.t1 <- linearHypothesis(table2.4,"shr.dep.out_2001 = shr.dep.in_2001",test="F")

> table2.4.t2 <- linearHypothesis(table2.4,"shr.aff.out_2001 = shr.aff.in_2001",test="F")

> table2.4.t3 <- linearHypothesis(table2.4,c("shr.dep.out_2001 = shr.dep.in_2001","shr.aff.out_2001 = shr.aff.in_2001"),test="F")

> table2.5.t1 <- linearHypothesis(table2.5,"shr.dep.out_2001 = shr.dep.in_2001",test="F")

> table2.5.t2 <- linearHypothesis(table2.5,"shr.aff.out_2001 = shr.aff.in_2001",test="F")

> table2.5.t3 <- linearHypothesis(table2.5,c("shr.dep.out_2001 = shr.dep.in_2001","shr.aff.out_2001 = shr.aff.in_2001"),test="F")

> # Format the output of the statistical tests
> table2.1.t1.out <- paste( Trim(table2.1.t1$F[2],digits=2), Stars(table2.1.t1[6][2,1]) )

> table2.2.t1.out <- paste( Trim(table2.2.t1$F[2],digits=2), Stars(table2.2.t1[6][2,1]) )

> table2.3.t1.out <- paste( Trim(table2.3.t1$F[2],digits=2), Stars(table2.3.t1[6][2,1]) )

> table2.4.t1.out <- paste( Trim(table2.4.t1$F[2],digits=2), Stars(table2.4.t1[6][2,1]) )

> table2.5.t1.out <- paste( Trim(table2.5.t1$F[2],digits=2), Stars(table2.5.t1[6][2,1]) )

> table2.1.t2.out <- paste( Trim(table2.1.t2$F[2],digits=2), Stars(table2.1.t2[6][2,1]) )

> table2.2.t2.out <- paste( Trim(table2.2.t2$F[2],digits=2), Stars(table2.2.t2[6][2,1]) )

> table2.3.t2.out <- paste( Trim(table2.3.t2$F[2],digits=2), Stars(table2.3.t2[6][2,1]) )

> table2.4.t2.out <- paste( Trim(table2.4.t2$F[2],digits=2), Stars(table2.4.t2[6][2,1]) )

> table2.5.t2.out <- paste( Trim(table2.5.t2$F[2],digits=2), Stars(table2.5.t2[6][2,1]) )

> table2.1.t3.out <- paste( Trim(table2.1.t3$F[2],digits=2), Stars(table2.1.t3[6][2,1]) )

> table2.2.t3.out <- paste( Trim(table2.2.t3$F[2],digits=2), Stars(table2.2.t3[6][2,1]) )

> table2.3.t3.out <- paste( Trim(table2.3.t3$F[2],digits=2), Stars(table2.3.t3[6][2,1]) )

> table2.4.t3.out <- paste( Trim(table2.4.t3$F[2],digits=2), Stars(table2.4.t3[6][2,1]) )

> table2.5.t3.out <- paste( Trim(table2.5.t3$F[2],digits=2), Stars(table2.5.t3[6][2,1]) )

> table2.t1 <- cbind( "T1", table2.1.t1.out, table2.2.t1.out, table2.3.t1.out, table2.4.t1.out, table2.5.t1.out)

> table2.t2 <- cbind( "T2", table2.1.t2.out, table2.2.t2.out, table2.3.t2.out, table2.4.t2.out, table2.5.t2.out)

> table2.t3 <- cbind( "T3", table2.1.t3.out, table2.2.t3.out, table2.3.t3.out, table2.4.t3.out, table2.5.t3.out)

> # Assemble the pieces of the output table
> # This variable will contain all of the information displayed in table 2
> table2.output <- rbind( table .... [TRUNCATED] 

> #######################################################################################
> ### Table 3 - Threshold Estimations for CRA
> ############ .... [TRUNCATED] 

> table3.b <- lm(formula=hidti_20046 ~ as.factor(MSA),
+                data=myData,
+                weights=loans_20046,
+                subset=( M .... [TRUNCATED] 

> table3.c <- lm(formula=hirate_20046 ~ as.factor(MSA),
+                data=myData,
+                weights=loans_20046,
+                subset=(  .... [TRUNCATED] 

> table3.d <- lm(formula=shr.dep.in_20046 ~ as.factor(MSA),
+                data=myData,
+                weights=loans_20046,
+                subse .... [TRUNCATED] 

> table3.e <- lm(formula=pur.dep.in_20046 ~ as.factor(MSA),
+                data=myData,
+                weights=loans_20046,
+                subse .... [TRUNCATED] 

> # table3.1 to table3.6 estimate the models presented in Table 3
> table3.1 <- lm(formula=delrate ~ cra_75 + cra_76 + cra_77 + cra_78 + cra_79 + cra_ .... [TRUNCATED] 

> table3.2 <- lm(formula=delrate ~ cra_75 + cra_76 + cra_77 + cra_78 + cra_79 + cra_80 + cra_81 + cra_82 + cra_83 +
+                                  .... [TRUNCATED] 

> table3.3 <- lm(formula=hidti_20046 ~ cra_75 + cra_76 + cra_77 + cra_78 + cra_79 + cra_80 + cra_81 + cra_82 + cra_83 +
+                              .... [TRUNCATED] 

> table3.4 <- lm(formula=hirate_20046 ~ cra_75 + cra_76 + cra_77 + cra_78 + cra_79 + cra_80 + cra_81 + cra_82 + cra_83 +
+                             .... [TRUNCATED] 

> table3.5 <- lm(formula=shr.dep.in_20046 ~ cra_75 + cra_76 + cra_77 + cra_78 + cra_79 + cra_80 + cra_81 + cra_82 + cra_83 +
+                         .... [TRUNCATED] 

> table3.6 <- lm(formula=pur.dep.in_20046 ~ cra_75 + cra_76 + cra_77 + cra_78 + cra_79 + cra_80 + cra_81 + cra_82 + cra_83 +
+                         .... [TRUNCATED] 

> # Assemble the coefficients into an output table
> table3.1.coefs <- makeTable( table3.1, numPlaces=10 )

> table3.2.coefs <- makeTable( table3.2, numPlaces=10 )

> table3.3.coefs <- makeTable( table3.3, numPlaces=10 )

> table3.4.coefs <- makeTable( table3.4, numPlaces=10 )

> table3.5.coefs <- makeTable( table3.5, numPlaces=10 )

> table3.6.coefs <- makeTable( table3.6, numPlaces=10 )

> table3.names <- dimnames( summary(table3.1)$coefficients )[[1]][2:10]

> table3.coefs <- cbind( table3.names, table3.1.coefs, table3.2.coefs, table3.3.coefs, table3.4.coefs, table3.5.coefs, table3.6.coefs )

> # This next block of code calculates the within-MSA R-squared for each estimation, based
> # on the SSEs from each estimation and from the models es .... [TRUNCATED] 

> table3.b.sse <- weighted.mean(x=table3.b$residuals^2, w=table3.b$weights)*length(table3.b$weights)

> table3.c.sse <- weighted.mean(x=table3.c$residuals^2, w=table3.c$weights)*length(table3.c$weights)

> table3.d.sse <- weighted.mean(x=table3.d$residuals^2, w=table3.d$weights)*length(table3.d$weights)

> table3.e.sse <- weighted.mean(x=table3.e$residuals^2, w=table3.e$weights)*length(table3.e$weights)

> table3.1.sse <- weighted.mean(x=table3.1$residuals^2, w=table3.1$weights)*length(table3.1$residuals)

> table3.2.sse <- weighted.mean(x=table3.2$residuals^2, w=table3.2$weights)*length(table3.2$residuals)

> table3.3.sse <- weighted.mean(x=table3.3$residuals^2, w=table3.3$weights)*length(table3.3$residuals)

> table3.4.sse <- weighted.mean(x=table3.4$residuals^2, w=table3.4$weights)*length(table3.4$residuals)

> table3.5.sse <- weighted.mean(x=table3.5$residuals^2, w=table3.5$weights)*length(table3.5$residuals)

> table3.6.sse <- weighted.mean(x=table3.6$residuals^2, w=table3.6$weights)*length(table3.6$residuals)

> table3.1.wR2 <- Trim( 1 - table3.1.sse/table3.a.sse, digits=3 )

> table3.2.wR2 <- Trim( 1 - table3.2.sse/table3.a.sse, digits=3 )

> table3.3.wR2 <- Trim( 1 - table3.3.sse/table3.b.sse, digits=3 )

> table3.4.wR2 <- Trim( 1 - table3.4.sse/table3.c.sse, digits=3 )

> table3.5.wR2 <- Trim( 1 - table3.5.sse/table3.d.sse, digits=3 )

> table3.6.wR2 <- Trim( 1 - table3.6.sse/table3.e.sse, digits=3 )

> table3.wR2 <- cbind( "R-squared", table3.1.wR2, table3.2.wR2, table3.3.wR2, table3.4.wR2, table3.5.wR2, table3.6.wR2 )

> # Report the number of observations in each estimation
> table3.1.obs <- length(table3.1$model[,1])

> table3.2.obs <- length(table3.2$model[,2])

> table3.3.obs <- length(table3.3$model[,2])

> table3.4.obs <- length(table3.4$model[,2])

> table3.5.obs <- length(table3.5$model[,2])

> table3.6.obs <- length(table3.6$model[,2])

> table3.obs <- cbind( "Observations", table3.1.obs, table3.2.obs, table3.3.obs, table3.4.obs, table3.5.obs, table3.6.obs)

> # Report the mean of the dependent variable in each estimation
> table3.1.ybar <- Trim( weighted.mean(x=table3.1$model[,1], w=table3.1$weights), dig .... [TRUNCATED] 

> table3.2.ybar <- Trim( weighted.mean(x=table3.2$model[,1], w=table3.2$weights), digits=3 )

> table3.3.ybar <- Trim( weighted.mean(x=table3.3$model[,1], w=table3.3$weights), digits=3 )

> table3.4.ybar <- Trim( weighted.mean(x=table3.4$model[,1], w=table3.4$weights), digits=3 )

> table3.5.ybar <- Trim( weighted.mean(x=table3.5$model[,1], w=table3.5$weights), digits=3 )

> table3.6.ybar <- Trim( weighted.mean(x=table3.6$model[,1], w=table3.6$weights), digits=3 )

> table3.ybar <- cbind( "Mean Y", table3.1.ybar, table3.2.ybar, table3.3.ybar, table3.4.ybar, table3.5.ybar, table3.6.ybar)

> # Conduct the three statistical tests (t1 and t2) for each specification
> table3.1.t1 <- linearHypothesis(table3.1,"2*cra_79 = cra_78 + cra_80",tes .... [TRUNCATED] 

> table3.1.t2 <- linearHypothesis(table3.1,"8*cra_79 = cra_75 + cra_76 + cra_77 + cra_78 + cra_80 + cra_81 + cra_82 + cra_83")

> table3.2.t1 <- linearHypothesis(table3.2,"2*cra_79 = cra_78 + cra_80",test="F")

> table3.2.t2 <- linearHypothesis(table3.2,"8*cra_79 = cra_75 + cra_76 + cra_77 + cra_78 + cra_80 + cra_81 + cra_82 + cra_83")

> table3.3.t1 <- linearHypothesis(table3.3,"2*cra_79 = cra_78 + cra_80",test="F")

> table3.3.t2 <- linearHypothesis(table3.3,"8*cra_79 = cra_75 + cra_76 + cra_77 + cra_78 + cra_80 + cra_81 + cra_82 + cra_83")

> table3.4.t1 <- linearHypothesis(table3.4,"2*cra_79 = cra_78 + cra_80",test="F")

> table3.4.t2 <- linearHypothesis(table3.4,"8*cra_79 = cra_75 + cra_76 + cra_77 + cra_78 + cra_80 + cra_81 + cra_82 + cra_83")

> table3.5.t1 <- linearHypothesis(table3.5,"2*cra_79 = cra_78 + cra_80",test="F")

> table3.5.t2 <- linearHypothesis(table3.5,"8*cra_79 = cra_75 + cra_76 + cra_77 + cra_78 + cra_80 + cra_81 + cra_82 + cra_83")

> table3.6.t1 <- linearHypothesis(table3.6,"2*cra_79 = cra_78 + cra_80",test="F")

> table3.6.t2 <- linearHypothesis(table3.6,"8*cra_79 = cra_75 + cra_76 + cra_77 + cra_78 + cra_80 + cra_81 + cra_82 + cra_83")

> # Format the output of the statistical tests
> table3.1.t1.out <- paste( Trim(table3.1.t1$F[2],digits=2), Stars(table3.1.t1[6][2,1]) )

> table3.2.t1.out <- paste( Trim(table3.2.t1$F[2],digits=2), Stars(table3.2.t1[6][2,1]) )

> table3.3.t1.out <- paste( Trim(table3.3.t1$F[2],digits=2), Stars(table3.3.t1[6][2,1]) )

> table3.4.t1.out <- paste( Trim(table3.4.t1$F[2],digits=2), Stars(table3.4.t1[6][2,1]) )

> table3.5.t1.out <- paste( Trim(table3.5.t1$F[2],digits=2), Stars(table3.5.t1[6][2,1]) )

> table3.6.t1.out <- paste( Trim(table3.6.t1$F[2],digits=2), Stars(table3.6.t1[6][2,1]) )

> table3.1.t2.out <- paste( Trim(table3.1.t2$F[2],digits=2), Stars(table3.1.t2[6][2,1]) )

> table3.2.t2.out <- paste( Trim(table3.2.t2$F[2],digits=2), Stars(table3.2.t2[6][2,1]) )

> table3.3.t2.out <- paste( Trim(table3.3.t2$F[2],digits=2), Stars(table3.3.t2[6][2,1]) )

> table3.4.t2.out <- paste( Trim(table3.4.t2$F[2],digits=2), Stars(table3.4.t2[6][2,1]) )

> table3.5.t2.out <- paste( Trim(table3.5.t2$F[2],digits=2), Stars(table3.5.t2[6][2,1]) )

> table3.6.t2.out <- paste( Trim(table3.6.t2$F[2],digits=2), Stars(table3.6.t2[6][2,1]) )

> table3.t1 <- cbind( "T1", table3.1.t1.out, table3.2.t1.out, table3.3.t1.out, table3.4.t1.out, table3.5.t1.out, table3.6.t1.out)

> table3.t2 <- cbind( "T2", table3.1.t2.out, table3.2.t2.out, table3.3.t2.out, table3.4.t2.out, table3.5.t2.out, table3.6.t2.out)

> # Assemble the pieces of the output table
> # This variable will contain all of the information displayed in table 2
> table3.output <- rbind( table .... [TRUNCATED] 

> #######################################################################################
> ### Table 4 - Threshold Estimations for GSE Income
> ##### .... [TRUNCATED] 

> table4.b <- lm(formula=hidti_20046 ~ as.factor(MSA),
+                data=myData,
+                weights=loans_20046,
+                subset=( M .... [TRUNCATED] 

> table4.c <- lm(formula=hirate_20046 ~ as.factor(MSA),
+                data=myData,
+                weights=loans_20046,
+                subset=(  .... [TRUNCATED] 

> table4.d <- lm(formula=gse_20046 ~ as.factor(MSA),
+                data=myData,
+                weights=loans_20046,
+                subset=( MIN .... [TRUNCATED] 

> # table4.1 to table4.5 estimate the models presented in Table 4
> table4.1 <- lm(formula=delrate ~ gsei_85 + gsei_86 + gsei_87 + gsei_88 + gsei_89 + .... [TRUNCATED] 

> table4.2 <- lm(formula=delrate ~ gsei_85 + gsei_86 + gsei_87 + gsei_88 + gsei_89 + gsei_90 + gsei_91 + gsei_92 + gsei_93 +
+                  SCORE2 .... [TRUNCATED] 

> table4.3 <- lm(formula=hidti_20046 ~ gsei_85 + gsei_86 + gsei_87 + gsei_88 + gsei_89 + gsei_90 + gsei_91 + gsei_92 + gsei_93 +
+                  SC .... [TRUNCATED] 

> table4.4 <- lm(formula=hirate_20046 ~ gsei_85 + gsei_86 + gsei_87 + gsei_88 + gsei_89 + gsei_90 + gsei_91 + gsei_92 + gsei_93 +
+                  S .... [TRUNCATED] 

> table4.5 <- lm(formula=gse_20046 ~ gsei_85 + gsei_86 + gsei_87 + gsei_88 + gsei_89 + gsei_90 + gsei_91 + gsei_92 + gsei_93 +
+                  SCOR .... [TRUNCATED] 

> # Assemble the coefficients into an output table
> table4.1.coefs <- makeTable( table4.1, numPlaces=10 )

> table4.2.coefs <- makeTable( table4.2, numPlaces=10 )

> table4.3.coefs <- makeTable( table4.3, numPlaces=10 )

> table4.4.coefs <- makeTable( table4.4, numPlaces=10 )

> table4.5.coefs <- makeTable( table4.5, numPlaces=10 )

> table4.names <- dimnames( summary(table4.1)$coefficients )[[1]][2:10]

> table4.coefs <- cbind( table4.names, table4.1.coefs, table4.2.coefs, table4.3.coefs, table4.4.coefs, table4.5.coefs )

> # This next block of code calculates the within-MSA R-squared for each estimation, based
> # on the SSEs from each estimation and from the models es .... [TRUNCATED] 

> table4.b.sse <- weighted.mean(x=table4.b$residuals^2, w=table4.b$weights)*length(table4.b$weights)

> table4.c.sse <- weighted.mean(x=table4.c$residuals^2, w=table4.c$weights)*length(table4.c$weights)

> table4.d.sse <- weighted.mean(x=table4.d$residuals^2, w=table4.d$weights)*length(table4.d$weights)

> table4.1.sse <- weighted.mean(x=table4.1$residuals^2, w=table4.1$weights)*length(table4.1$residuals)

> table4.2.sse <- weighted.mean(x=table4.2$residuals^2, w=table4.2$weights)*length(table4.2$residuals)

> table4.3.sse <- weighted.mean(x=table4.3$residuals^2, w=table4.3$weights)*length(table4.3$residuals)

> table4.4.sse <- weighted.mean(x=table4.4$residuals^2, w=table4.4$weights)*length(table4.4$residuals)

> table4.5.sse <- weighted.mean(x=table4.5$residuals^2, w=table4.5$weights)*length(table4.5$residuals)

> table4.1.wR2 <- Trim( 1 - table4.1.sse/table4.a.sse, digits=3 )

> table4.2.wR2 <- Trim( 1 - table4.2.sse/table4.a.sse, digits=3 )

> table4.3.wR2 <- Trim( 1 - table4.3.sse/table4.b.sse, digits=3 )

> table4.4.wR2 <- Trim( 1 - table4.4.sse/table4.c.sse, digits=3 )

> table4.5.wR2 <- Trim( 1 - table4.5.sse/table4.d.sse, digits=3 )

> table4.wR2 <- cbind( "R-squared", table4.1.wR2, table4.2.wR2, table4.3.wR2, table4.4.wR2, table4.5.wR2 )

> # Report the number of observations in each estimation
> table4.1.obs <- length(table4.1$model[,1])

> table4.2.obs <- length(table4.2$model[,2])

> table4.3.obs <- length(table4.3$model[,2])

> table4.4.obs <- length(table4.4$model[,2])

> table4.5.obs <- length(table4.5$model[,2])

> table4.obs <- cbind( "Observations", table4.1.obs, table4.2.obs, table4.3.obs, table4.4.obs, table4.5.obs)

> # Report the mean of the dependent variable in each estimation
> table4.1.ybar <- Trim( weighted.mean(x=table4.1$model[,1], w=table4.1$weights), dig .... [TRUNCATED] 

> table4.2.ybar <- Trim( weighted.mean(x=table4.2$model[,1], w=table4.2$weights), digits=3 )

> table4.3.ybar <- Trim( weighted.mean(x=table4.3$model[,1], w=table4.3$weights), digits=3 )

> table4.4.ybar <- Trim( weighted.mean(x=table4.4$model[,1], w=table4.4$weights), digits=3 )

> table4.5.ybar <- Trim( weighted.mean(x=table4.5$model[,1], w=table4.5$weights), digits=3 )

> table4.ybar <- cbind( "Mean Y", table4.1.ybar, table4.2.ybar, table4.3.ybar, table4.4.ybar, table4.5.ybar)

> # Conduct the three statistical tests (t1 and t2) for each specification
> table4.1.t1 <- linearHypothesis(table4.1,"2*gsei_89 = gsei_88 + gsei_90", .... [TRUNCATED] 

> table4.1.t2 <- linearHypothesis(table4.1,"8*gsei_89 = gsei_85 + gsei_86 + gsei_87 + gsei_88 + gsei_90 + gsei_91 + gsei_92 + gsei_93")

> table4.2.t1 <- linearHypothesis(table4.2,"2*gsei_89 = gsei_88 + gsei_90",test="F")

> table4.2.t2 <- linearHypothesis(table4.2,"8*gsei_89 = gsei_85 + gsei_86 + gsei_87 + gsei_88 + gsei_90 + gsei_91 + gsei_92 + gsei_93")

> table4.3.t1 <- linearHypothesis(table4.3,"2*gsei_89 = gsei_88 + gsei_90",test="F")

> table4.3.t2 <- linearHypothesis(table4.3,"8*gsei_89 = gsei_85 + gsei_86 + gsei_87 + gsei_88 + gsei_90 + gsei_91 + gsei_92 + gsei_93")

> table4.4.t1 <- linearHypothesis(table4.4,"2*gsei_89 = gsei_88 + gsei_90",test="F")

> table4.4.t2 <- linearHypothesis(table4.4,"8*gsei_89 = gsei_85 + gsei_86 + gsei_87 + gsei_88 + gsei_90 + gsei_91 + gsei_92 + gsei_93")

> table4.5.t1 <- linearHypothesis(table4.5,"2*gsei_89 = gsei_88 + gsei_90",test="F")

> table4.5.t2 <- linearHypothesis(table4.5,"8*gsei_89 = gsei_85 + gsei_86 + gsei_87 + gsei_88 + gsei_90 + gsei_91 + gsei_92 + gsei_93")

> # Format the output of the statistical tests
> table4.1.t1.out <- paste( Trim(table4.1.t1$F[2],digits=2), Stars(table4.1.t1[6][2,1]) )

> table4.2.t1.out <- paste( Trim(table4.2.t1$F[2],digits=2), Stars(table4.2.t1[6][2,1]) )

> table4.3.t1.out <- paste( Trim(table4.3.t1$F[2],digits=2), Stars(table4.3.t1[6][2,1]) )

> table4.4.t1.out <- paste( Trim(table4.4.t1$F[2],digits=2), Stars(table4.4.t1[6][2,1]) )

> table4.5.t1.out <- paste( Trim(table4.5.t1$F[2],digits=2), Stars(table4.5.t1[6][2,1]) )

> table4.1.t2.out <- paste( Trim(table4.1.t2$F[2],digits=2), Stars(table4.1.t2[6][2,1]) )

> table4.2.t2.out <- paste( Trim(table4.2.t2$F[2],digits=2), Stars(table4.2.t2[6][2,1]) )

> table4.3.t2.out <- paste( Trim(table4.3.t2$F[2],digits=2), Stars(table4.3.t2[6][2,1]) )

> table4.4.t2.out <- paste( Trim(table4.4.t2$F[2],digits=2), Stars(table4.4.t2[6][2,1]) )

> table4.5.t2.out <- paste( Trim(table4.5.t2$F[2],digits=2), Stars(table4.5.t2[6][2,1]) )

> table4.t1 <- cbind( "T1", table4.1.t1.out, table4.2.t1.out, table4.3.t1.out, table4.4.t1.out, table4.5.t1.out)

> table4.t2 <- cbind( "T2", table4.1.t2.out, table4.2.t2.out, table4.3.t2.out, table4.4.t2.out, table4.5.t2.out)

> # Assemble the pieces of the output table
> # This variable will contain all of the information displayed in table 2
> table4.output <- rbind( table .... [TRUNCATED] 

> #######################################################################################
> ### Table 5 - Threshold Estimations for GSE Minority
> ### .... [TRUNCATED] 

> table5.b <- lm(formula=hidti_20046 ~ as.factor(MSA),
+                data=myData,
+                weights=loans_20046,
+                subset=( M .... [TRUNCATED] 

> table5.c <- lm(formula=hirate_20046 ~ as.factor(MSA),
+                data=myData,
+                weights=loans_20046,
+                subset=(  .... [TRUNCATED] 

> table5.d <- lm(formula=gse_20046 ~ as.factor(MSA),
+                data=myData,
+                weights=loans_20046,
+                subset=( MIN .... [TRUNCATED] 

> # table5.1 to table5.5 estimate the models presented in Table 5
> table5.1 <- lm(formula=delrate ~ gsem_34 + gsem_33 + gsem_32 + gsem_31 + gsem_30 + .... [TRUNCATED] 

> table5.2 <- lm(formula=delrate ~ gsem_34 + gsem_33 + gsem_32 + gsem_31 + gsem_30 + gsem_29 + gsem_28 + gsem_27 + gsem_26 +
+                  SCORE2 .... [TRUNCATED] 

> table5.3 <- lm(formula=hidti_20046 ~ gsem_34 + gsem_33 + gsem_32 + gsem_31 + gsem_30 + gsem_29 + gsem_28 + gsem_27 + gsem_26 +
+                  SC .... [TRUNCATED] 

> table5.4 <- lm(formula=hirate_20046 ~ gsem_34 + gsem_33 + gsem_32 + gsem_31 + gsem_30 + gsem_29 + gsem_28 + gsem_27 + gsem_26 +
+                  S .... [TRUNCATED] 

> table5.5 <- lm(formula=gse_20046 ~ gsem_34 + gsem_33 + gsem_32 + gsem_31 + gsem_30 + gsem_29 + gsem_28 + gsem_27 + gsem_26 +
+                  SCOR .... [TRUNCATED] 

> # Assemble the coefficients into an output table
> table5.1.coefs <- makeTable( table5.1, numPlaces=10 )

> table5.2.coefs <- makeTable( table5.2, numPlaces=10 )

> table5.3.coefs <- makeTable( table5.3, numPlaces=10 )

> table5.4.coefs <- makeTable( table5.4, numPlaces=10 )

> table5.5.coefs <- makeTable( table5.5, numPlaces=10 )

> table5.names <- dimnames( summary(table5.1)$coefficients )[[1]][2:10]

> table5.coefs <- cbind( table5.names, table5.1.coefs, table5.2.coefs, table5.3.coefs, table5.4.coefs, table5.5.coefs )

> # This next block of code calculates the within-MSA R-squared for each estimation, based
> # on the SSEs from each estimation and from the models es .... [TRUNCATED] 

> table5.b.sse <- weighted.mean(x=table5.b$residuals^2, w=table5.b$weights)*length(table5.b$weights)

> table5.c.sse <- weighted.mean(x=table5.c$residuals^2, w=table5.c$weights)*length(table5.c$weights)

> table5.d.sse <- weighted.mean(x=table5.d$residuals^2, w=table5.d$weights)*length(table5.d$weights)

> table5.1.sse <- weighted.mean(x=table5.1$residuals^2, w=table5.1$weights)*length(table5.1$residuals)

> table5.2.sse <- weighted.mean(x=table5.2$residuals^2, w=table5.2$weights)*length(table5.2$residuals)

> table5.3.sse <- weighted.mean(x=table5.3$residuals^2, w=table5.3$weights)*length(table5.3$residuals)

> table5.4.sse <- weighted.mean(x=table5.4$residuals^2, w=table5.4$weights)*length(table5.4$residuals)

> table5.5.sse <- weighted.mean(x=table5.5$residuals^2, w=table5.5$weights)*length(table5.5$residuals)

> table5.1.wR2 <- Trim( 1 - table5.1.sse/table5.a.sse, digits=3 )

> table5.2.wR2 <- Trim( 1 - table5.2.sse/table5.a.sse, digits=3 )

> table5.3.wR2 <- Trim( 1 - table5.3.sse/table5.b.sse, digits=3 )

> table5.4.wR2 <- Trim( 1 - table5.4.sse/table5.c.sse, digits=3 )

> table5.5.wR2 <- Trim( 1 - table5.5.sse/table5.d.sse, digits=3 )

> table5.wR2 <- cbind( "R-squared", table5.1.wR2, table5.2.wR2, table5.3.wR2, table5.4.wR2, table5.5.wR2 )

> # Report the number of observations in each estimation
> table5.1.obs <- length(table5.1$model[,1])

> table5.2.obs <- length(table5.2$model[,2])

> table5.3.obs <- length(table5.3$model[,2])

> table5.4.obs <- length(table5.4$model[,2])

> table5.5.obs <- length(table5.5$model[,2])

> table5.obs <- cbind( "Observations", table5.1.obs, table5.2.obs, table5.3.obs, table5.4.obs, table5.5.obs)

> # Report the mean of the dependent variable in each estimation
> table5.1.ybar <- Trim( weighted.mean(x=table5.1$model[,1], w=table5.1$weights), dig .... [TRUNCATED] 

> table5.2.ybar <- Trim( weighted.mean(x=table5.2$model[,1], w=table5.2$weights), digits=3 )

> table5.3.ybar <- Trim( weighted.mean(x=table5.3$model[,1], w=table5.3$weights), digits=3 )

> table5.4.ybar <- Trim( weighted.mean(x=table5.4$model[,1], w=table5.4$weights), digits=3 )

> table5.5.ybar <- Trim( weighted.mean(x=table5.5$model[,1], w=table5.5$weights), digits=3 )

> table5.ybar <- cbind( "Mean Y", table5.1.ybar, table5.2.ybar, table5.3.ybar, table5.4.ybar, table5.5.ybar)

> # Conduct the three statistical tests (t1 and t2) for each specification
> table5.1.t1 <- linearHypothesis(table5.1,"2*gsem_29 = gsem_28 + gsem_30", .... [TRUNCATED] 

> table5.1.t2 <- linearHypothesis(table5.1,"8*gsem_29 = gsem_26 + gsem_27 + gsem_28 + gsem_30 + gsem_31 + gsem_32 + gsem_33 + gsem_34")

> table5.2.t1 <- linearHypothesis(table5.2,"2*gsem_29 = gsem_28 + gsem_30",test="F")

> table5.2.t2 <- linearHypothesis(table5.2,"8*gsem_29 = gsem_26 + gsem_27 + gsem_28 + gsem_30 + gsem_31 + gsem_32 + gsem_33 + gsem_34")

> table5.3.t1 <- linearHypothesis(table5.3,"2*gsem_29 = gsem_28 + gsem_30",test="F")

> table5.3.t2 <- linearHypothesis(table5.3,"8*gsem_29 = gsem_26 + gsem_27 + gsem_28 + gsem_30 + gsem_31 + gsem_32 + gsem_33 + gsem_34")

> table5.4.t1 <- linearHypothesis(table5.4,"2*gsem_29 = gsem_28 + gsem_30",test="F")

> table5.4.t2 <- linearHypothesis(table5.4,"8*gsem_29 = gsem_26 + gsem_27 + gsem_28 + gsem_30 + gsem_31 + gsem_32 + gsem_33 + gsem_34")

> table5.5.t1 <- linearHypothesis(table5.5,"2*gsem_29 = gsem_28 + gsem_30",test="F")

> table5.5.t2 <- linearHypothesis(table5.5,"8*gsem_29 = gsem_26 + gsem_27 + gsem_28 + gsem_30 + gsem_31 + gsem_32 + gsem_33 + gsem_34")

> # Format the output of the statistical tests
> table5.1.t1.out <- paste( Trim(table5.1.t1$F[2],digits=2), Stars(table5.1.t1[6][2,1]) )

> table5.2.t1.out <- paste( Trim(table5.2.t1$F[2],digits=2), Stars(table5.2.t1[6][2,1]) )

> table5.3.t1.out <- paste( Trim(table5.3.t1$F[2],digits=2), Stars(table5.3.t1[6][2,1]) )

> table5.4.t1.out <- paste( Trim(table5.4.t1$F[2],digits=2), Stars(table5.4.t1[6][2,1]) )

> table5.5.t1.out <- paste( Trim(table5.5.t1$F[2],digits=2), Stars(table5.5.t1[6][2,1]) )

> table5.1.t2.out <- paste( Trim(table5.1.t2$F[2],digits=2), Stars(table5.1.t2[6][2,1]) )

> table5.2.t2.out <- paste( Trim(table5.2.t2$F[2],digits=2), Stars(table5.2.t2[6][2,1]) )

> table5.3.t2.out <- paste( Trim(table5.3.t2$F[2],digits=2), Stars(table5.3.t2[6][2,1]) )

> table5.4.t2.out <- paste( Trim(table5.4.t2$F[2],digits=2), Stars(table5.4.t2[6][2,1]) )

> table5.5.t2.out <- paste( Trim(table5.5.t2$F[2],digits=2), Stars(table5.5.t2[6][2,1]) )

> table5.t1 <- cbind( "T1", table5.1.t1.out, table5.2.t1.out, table5.3.t1.out, table5.4.t1.out, table5.5.t1.out)

> table5.t2 <- cbind( "T2", table5.1.t2.out, table5.2.t2.out, table5.3.t2.out, table5.4.t2.out, table5.5.t2.out)

> # Assemble the pieces of the output table
> # This variable will contain all of the information displayed in table 2
> table5.output <- rbind( table .... [TRUNCATED] 

> # print out the tables that appear in the paper
> print(table1.output)
              [,1]                            [,2]    
table1.output "2008 Delinquency Rate"         "7.17"  
              "Center City"                   "0.41"  
              "Median House Value"            "0.5"   
              "Median Age"                    "35.25" 
              "% 65 or Older"                 "11.93" 
              "Unemployment Rate"             "4.99"  
              "% Minority"                    "27.89" 
              "Owner Occupancy Rate"          "67.18" 
              "Vacancy Rate"                  "5.94"  
              "2000 Relative Income"          "100.55"
              "1990 Relative Income"          "142.9" 
              "2001 Credit Score"             "708.44"
              "2004 Credit Score"             "710.98"
              "% High PTI"                    "14.35" 
              "% High Rate"                   "21.66" 
              "% Piggyback"                   "9.29"  
              "% No Income"                   "6.05"  
              "% LMI"                         "28.35" 
              "% Below Median Income"         "42.95" 
              "2001 Dep Out Share"            "12.1"  
              "2001 Dep In Share"             "22.4"  
              "2001 Aff Out Share"            "21.63" 
              "2001 Aff In Share"             "9.96"  
              "2001 Credit Union Share"       "3"     
              "2001 Independent Share"        "30.91" 
              "2004-6 Dep Out Share"          "18.75" 
              "2004-6 Dep In Share"           "21.79" 
              "2004-6 Aff Out Share"          "17.14" 
              "2004-6 Aff In Share"           "5.64"  
              "2004-6 Credit Union Share"     "2.99"  
              "2004-6 Independent Share"      "33.69" 
              "2001 Dep Out Purchases"        "7.15"  
              "2001 Dep In Purchases"         "2.29"  
              "2001 Aff Out Purchases"        "7.95"  
              "2001 Aff In Purchases"         "3.29"  
              "2001 Credit Union Purchases"   "0.01"  
              "2001 Independent Purchases"    "9.43"  
              "2001 GSE Sales"                "39.93" 
              "2004-6 Dep Out Purchases"      "12.41" 
              "2004-6 Dep In Purchases"       "4.4"   
              "2004-6 Aff Out Purchases"      "14.45" 
              "2004-6 Aff In Purchases"       "3.29"  
              "2004-6 Credit Union Purchases" "0.05"  
              "2004-6 Independent Purchases"  "8.62"  
              "2004-6 GSE Sales"              "25.65" 

> print(table2.output)
      table2.names       table2.1.coefs     table2.2.coefs     table2.3.coefs     table2.4.coefs    
 [1,] "shr.dep.out_2001" "-0.062 *** 0.008" "-0.124 *** 0.009" "-0.036 *** 0.009" "-0.084 *** 0.011"
 [2,] "shr.dep.in_2001"  "-0.063 *** 0.005" "-0.175 *** 0.007" "-0.027 *** 0.007" "-0.043 *** 0.007"
 [3,] "shr.aff.out_2001" "-0.035 *** 0.007" "-0.124 *** 0.010" "-0.037 *** 0.009" "-0.020 **  0.009"
 [4,] "shr.aff.in_2001"  "-0.073 *** 0.007" "-0.176 *** 0.012" "-0.057 *** 0.011" "-0.099 *** 0.011"
 [5,] "shr.cu_2001"      "-0.131 *** 0.015" "-0.312 *** 0.019" "-0.111 *** 0.018" "-0.064 *** 0.023"
 [6,] "pur.dep.out_2001" "-0.005     0.008" " 0.006     0.011" "-0.020 **  0.010" "-0.041 *** 0.011"
 [7,] "pur.dep.in_2001"  " 0.040 *** 0.012" "-0.060 *** 0.012" "-0.028 **  0.011" " 0.077 *** 0.018"
 [8,] "pur.aff.out_2001" " 0.045 *** 0.008" "-0.032 *** 0.010" "-0.037 *** 0.009" " 0.063 *** 0.012"
 [9,] "pur.aff.in_2001"  "-0.006     0.010" "-0.074 *** 0.015" "-0.070 *** 0.014" " 0.080 *** 0.015"
[10,] "pur.cu_2001"      " 0.234     0.302" "-0.313     0.206" "-0.446 **  0.186" " 0.516     0.425"
[11,] "pur.ind_2001"     "-0.001     0.007" " 0.142 *** 0.013" " 0.028 **  0.012" " 0.019 *   0.010"
[12,] "gse_2001"         "-0.003     0.005" "-0.097 *** 0.007" "-0.013 *   0.008" " 0.012 *   0.007"
[13,] "Observations"     "12003"            "12003"            "12003"            "12003"           
[14,] "R-squared"        "0.399"            "0.491"            "0.586"            "0.302"           
[15,] "Mean y"           "10.894"           "10.894"           "10.894"           "19.001"          
[16,] "T1"               "0.03    "         "31.37 ***"        "1.38    "         "13.93 ***"       
[17,] "T2"               "23.11 ***"        "15.48 ***"        "2.69    "         "48.42 ***"       
[18,] "T3"               "11.7 ***"         "25.68 ***"        "1.92    "         "29.6 ***"        
      table2.5.coefs    
 [1,] "-0.087 *** 0.014"
 [2,] "-0.219 *** 0.010"
 [3,] "-0.031 *** 0.012"
 [4,] "-0.144 *** 0.013"
 [5,] "-0.197 *** 0.029"
 [6,] " 0.064 *** 0.014"
 [7,] " 0.000     0.023"
 [8,] "-0.034 **  0.015"
 [9,] " 0.008     0.019"
[10,] "-0.259     0.545"
[11,] " 0.065 *** 0.013"
[12,] "-0.185 *** 0.009"
[13,] "12003"           
[14,] "0.681"           
[15,] "32.656"          
[16,] "88.61 ***"       
[17,] "59.92 ***"       
[18,] "79.39 ***"       

> print(table3.output)
      table3.names   table3.1.coefs     table3.2.coefs     table3.3.coefs     table3.4.coefs    
 [1,] "cra_75"       "-0.362 *   0.201" "-0.316 **  0.160" " 0.421     0.295" "-0.022     0.383"
 [2,] "cra_76"       "-0.274     0.193" "-0.079     0.154" "-0.306     0.283" "-0.124     0.367"
 [3,] "cra_77"       " 0.699 *** 0.189" " 0.304 **  0.151" " 0.230     0.279" " 0.630 *   0.362"
 [4,] "cra_78"       "-0.094     0.195" " 0.024     0.155" " 0.079     0.290" "-0.425     0.376"
 [5,] "cra_79"       "-0.052     0.192" "-0.027     0.152" "-0.025     0.289" " 0.053     0.375"
 [6,] "cra_80"       " 0.179     0.188" " 0.114     0.149" " 0.418     0.285" "-0.266     0.369"
 [7,] "cra_81"       "-0.202     0.184" "-0.036     0.147" "-0.135     0.279" " 0.338     0.361"
 [8,] "cra_82"       " 0.196     0.179" " 0.088     0.142" "-0.292     0.270" " 0.522     0.349"
 [9,] "cra_83"       "-0.238     0.171" "-0.220     0.136" " 0.621 **  0.260" "-0.552     0.337"
[10,] "Observations" "4766"             "4766"             "4766"             "4766"            
[11,] "R-squared"    "0.405"            "0.625"            "0.355"            "0.589"           
[12,] "Mean Y"       "8.972"            "8.972"            "17.445"           "27.093"          
[13,] "T1"           "0.1    "          "0.16    "         "0.36    "         "0.45    "        
[14,] "T2"           "0.03    "         "0    "            "0.22    "         "0.01    "        
      table3.5.coefs     table3.6.coefs    
 [1,] "-0.128     0.377" "-0.180     0.139"
 [2,] " 0.221     0.361" " 0.172     0.133"
 [3,] "-0.007     0.356" "-0.065     0.131"
 [4,] " 0.100     0.371" "-0.033     0.137"
 [5,] "-0.047     0.369" " 0.871 *** 0.136"
 [6,] "-0.505     0.363" "-0.080     0.134"
 [7,] " 1.287 *** 0.356" " 0.025     0.131"
 [8,] "-0.722 **  0.344" "-0.084     0.127"
 [9,] "-0.039     0.332" "-0.069     0.122"
[10,] "4766"             "4766"            
[11,] "0.199"            "0.066"           
[12,] "20.391"           "4.613"           
[13,] "0.07    "         "18.55 ***"       
[14,] "0.03    "         "34.88 ***"       

> print(table4.output)
      table4.names   table4.1.coefs     table4.2.coefs     table4.3.coefs     table4.4.coefs    
 [1,] "gsei_85"      "-0.120     0.151" "-0.080     0.122" " 0.278     0.219" "-0.062     0.355"
 [2,] "gsei_86"      "-0.334 **  0.143" "-0.345 *** 0.116" "-0.386 *   0.208" " 0.229     0.336"
 [3,] "gsei_87"      " 0.489 *** 0.136" " 0.331 *** 0.110" " 0.777 *** 0.197" "-0.226     0.319"
 [4,] "gsei_88"      "-0.051     0.138" "-0.047     0.112" "-0.062     0.201" " 0.523     0.326"
 [5,] "gsei_89"      " 0.104     0.137" " 0.225 **  0.111" "-0.180     0.200" "-0.352     0.323"
 [6,] "gsei_90"      " 0.049     0.133" "-0.064     0.108" " 0.061     0.197" " 0.616 *   0.318"
 [7,] "gsei_91"      "-0.052     0.130" "-0.069     0.106" " 0.186     0.192" " 0.065     0.310"
 [8,] "gsei_92"      "-0.026     0.128" "-0.034     0.104" " 0.071     0.188" "-0.213     0.305"
 [9,] "gsei_93"      " 0.130     0.127" " 0.124     0.103" "-0.043     0.187" " 0.240     0.302"
[10,] "Observations" "3684"             "3684"             "3684"             "3684"            
[11,] "R-squared"    "0.257"            "0.511"            "0.241"            "0.435"           
[12,] "Mean Y"       "6.003"            "6.003"            "12.711"           "21.791"          
[13,] "T1"           "0.24    "         "2.55    "         "0.32    "         "3.25 *  "        
[14,] "T2"           "0.36    "         "3.87 ** "         "1.64    "         "1.85    "        
      table4.5.coefs    
 [1,] " 0.468     0.324"
 [2,] "-0.363     0.307"
 [3,] "-0.284     0.291"
 [4,] "-0.471     0.298"
 [5,] " 0.402     0.295"
 [6,] "-0.034     0.291"
 [7,] "-0.298     0.283"
 [8,] " 0.416     0.278"
 [9,] "-0.404     0.276"
[10,] "3684"            
[11,] "0.153"           
[12,] "26.643"          
[13,] "1.97    "        
[14,] "2.45    "        

> print(table5.output)
      table5.names   table5.1.coefs     table5.2.coefs     table5.3.coefs     table5.4.coefs    
 [1,] "gsem_34"      "-0.514 *   0.290" "-0.191     0.221" "-0.908 **  0.412" "-0.877     0.553"
 [2,] "gsem_33"      " 0.049     0.271" " 0.103     0.206" "-0.045     0.376" "-0.261     0.506"
 [3,] "gsem_32"      " 0.128     0.293" "-0.084     0.222" " 0.372     0.408" " 0.297     0.549"
 [4,] "gsem_31"      " 0.838 *** 0.294" " 0.651 *** 0.225" " 0.923 **  0.415" " 0.938 *   0.557"
 [5,] "gsem_30"      "-0.374     0.277" "-0.464 **  0.211" "-0.203     0.389" "-0.064     0.523"
 [6,] "gsem_29"      " 0.207     0.260" " 0.326     0.198" " 0.555     0.361" " 1.243 **  0.486"
 [7,] "gsem_28"      " 0.057     0.254" " 0.136     0.194" "-0.313     0.356" "-1.918 *** 0.479"
 [8,] "gsem_27"      " 0.050     0.267" "-0.250     0.203" " 0.048     0.377" " 0.464     0.507"
 [9,] "gsem_26"      " 0.156     0.253" " 0.204     0.193" "-0.128     0.358" " 0.832 *   0.481"
[10,] "Observations" "1621"             "1621"             "1621"             "1621"            
[11,] "R-squared"    "0.262"            "0.576"            "0.276"            "0.513"           
[12,] "Mean Y"       "7.781"            "7.781"            "16.841"           "20.276"          
[13,] "T1"           "0.79    "         "2.45    "         "2.01    "         "8.42 ***"        
[14,] "T2"           "0.29    "         "1.95    "         "2.05    "         "5.71 ** "        
      table5.5.coefs    
 [1,] " 0.833 *   0.448"
 [2,] " 0.343     0.410"
 [3,] "-0.430     0.445"
 [4,] "-0.706     0.452"
 [5,] "-0.348     0.424"
 [6,] " 0.698 *   0.394"
 [7,] " 0.268     0.388"
 [8,] "-0.558     0.411"
 [9,] " 0.138     0.390"
[10,] "1621"            
[11,] "0.228"           
[12,] "24.051"          
[13,] "1.4    "         
[14,] "2.86 *  "        

> sink() # Close log file
